Secciones

Referencias

International aid may take the form of multilateral aid – provided through international bodies such as the UN, or NGOs such as Oxfam – or bilateral aid, which operates on a government-to-government basis. There is considerable debate about whether international aid works, in the sense of reducing poverty and stimulating development.

However, the effectiveness of aid is often diluted by corruption. Aid is invariably channeled through the governments of recipient countries, in which power is often concentrated in the hands of a few politicians and bureaucrats, and the mechanisms of accountability are, at best, poorly developed. This tends to benefit corrupt leaders and elites rather than the people, projects and programs for which it was intended.

Watts, Carl. (2014). Re: Does foreign aid help the developing countries towards development?. Retrieved from: https://www.researchgate.net/post/Does_foreign_aid_help_the_developing_countries_towards_development/5322005ed039b1e7648b459c/citation/download.

The hypothesis that foreign aid can promote growth in developing countries was explored, using panel data series for foreign aid, while accounting for regional differences in Asian, African, Latin American, and the Caribbean countries as well as the differences in income levels, the results of this study also indicate that foreign aid has mixed effects on economic growth in developing countries.

Ekanayake, E. & Chatrna, Dasha. (2010). The effect of foreign aid on economic growth in developing countries. Journal of International Business and Cultural Studies. 3.

This study examines the relationships between foreign aid, institutional structure, and economic performance for 80 countries in Europe, America, Africa, and Asia. It is found that official development assistance and the quality of institutional structure in the sample countries affect economic growth positively.

Hayaloğlu, Pınar. (2023). Foreign Aid, Institutions, and Economic Performance in Developing Countries. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi. 18. 748-765. 10.17153/oguiibf.1277348.

Manual para replicar

Cargando Librerias

Algunas librerias y paquetes usados para obtener y descargar los datos

library(tidyverse) # manejo de dataframes
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.2     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   2.0.1     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(WDI)       # libreria para acceder a metadata de banco mundial
library(readxl)    # leer archivos de excel
library(readr)     # leer archivos csv
library(visdat)    # visualizacion de datos como graficos
library(plotly)    # graficos
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(purrr)     # funcion map
library(plm)       # modelos lineales para datos panel
## 
## Attaching package: 'plm'
## The following objects are masked from 'package:dplyr':
## 
##     between, lag, lead
library(car)       # test y utilidaddes para modelos
## Loading required package: carData
## 
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
## 
##     recode
## The following object is masked from 'package:purrr':
## 
##     some

Obtener datos

Datos para paises bajos ingresos sean utilizados, segun clasificación del banco mundial, hay 26 paises de bajos ingresos y 51 de ingresos medios bajos

country_class <- read_excel("CLASS.xlsx")

country_class %>%
  filter(!is.na(Region), !is.na(`Income group`)) %>%
  group_by(`Income group`) %>%
  summarise(countries = n()) %>%
  arrange(factor(`Income group`, levels = c('High income', 'Upper middle income', 'Lower middle income', 'Low income')))

Listado de paises a analisar:

my_countries <- country_class %>%
  filter(!is.na(Region), `Income group` %in% c('Low income', 'Lower middle income')) %>%
  select(Code)
my_countries

Hacer la respectiva asociacion de nombres iso3c e iso2c

my_countries$iso2c <- WDI_data$country %>%
  filter(iso3c %in% my_countries$Code) %>%
  .$iso2c
my_countries

Datos del banco mundial (para ODA y los indices de gobernanza) y el Human Development Reports API son descargados desde scripts de Python. Son almacenados en archivos CSV y luego son cargados aqui:

cargar HDI

datos_HDI <- read_csv("datos_python_HDI.csv", col_names = c('Code', 'iso2c', 'indicator', 'year', 'value'), 
                      col_types = list(col_character(), col_character(), col_character(), col_double(), col_double()))

hdi_indicators <- datos_HDI %>% distinct(indicator) %>% .$indicator

cargar ODA, GDP, POP.GROW

oda_indicators <- c(
'DT_ODA_ALLD_CD',
'DT_ODA_ALLD_KD',
'DT_ODA_OATL_CD',
'DT_ODA_OATL_KD',
'DT_ODA_ODAT_CD',
'DT_ODA_ODAT_GI_ZS',
'DT_ODA_ODAT_GN_ZS',
'DT_ODA_ODAT_KD',
'DT_ODA_ODAT_MP_ZS',
'DT_ODA_ODAT_PC_ZS',
'DT_ODA_ODAT_XP_ZS'
)
gob_indicators <- c(
'CC_EST',
'CC_NO_SRC',
'CC_PER_RNK',
'CC_PER_RNK_LOWER',
'CC_PER_RNK_UPPER',
'CC_STD_ERR',
'GE_EST',
'GE_NO_SRC',
'GE_PER_RNK',
'GE_PER_RNK_LOWER',
'GE_PER_RNK_UPPER',
'GE_STD_ERR',
'PV_EST',
'PV_NO_SRC',
'PV_PER_RNK',
'PV_PER_RNK_LOWER',
'PV_PER_RNK_UPPER',
'PV_STD_ERR',
'RQ_EST',
'RQ_NO_SRC',
'RQ_PER_RNK',
'RQ_PER_RNK_LOWER',
'RQ_PER_RNK_UPPER',
'RQ_STD_ERR',
'RL_EST',
'RL_NO_SRC',
'RL_PER_RNK',
'RL_PER_RNK_LOWER',
'RL_PER_RNK_UPPER',
'RL_STD_ERR',
'VA_EST',
'VA_NO_SRC',
'VA_PER_RNK',
'VA_PER_RNK_LOWER',
'VA_PER_RNK_UPPER',
'VA_STD_ERR'
)
gdp_indicators <- c(
'NY_ADJ_NNTY_PC_CD',
'NY_ADJ_NNTY_PC_KD',
'NY_ADJ_NNTY_PC_KD_ZG',
'NY_GDP_PCAP_CN',
'NY_GDP_PCAP_KN',
'NY_GDP_PCAP_CD',
'NY_GDP_PCAP_KD',
'NY_GDP_MKTP_KD_ZG',
'NY_GDP_DEFL_ZS_AD',
'NY_GDP_DEFL_ZS',
'NY_GDP_MKTP_CD',
'NY_GDP_MKTP_CN',
'NY_GDP_MKTP_KN',
'NY_GDP_MKTP_KD',
'NY_GDP_PCAP_KD_ZG',
'NY_GDP_PCAP_PP_KD',
'NY_GDP_PCAP_PP_CD',
'SL_GDP_PCAP_EM_KD',
'SP_POP_GROW'
)

datos_WB <- data.frame(indicator = character(), iso2c = character(), year = double(), value = double())

suppressWarnings(
  for (indicator in c(oda_indicators, gob_indicators, gdp_indicators)) {
    datos_WB <- rbind(datos_WB, read_csv(paste("datos_python", indicator, ".csv", sep =''), 
                                           col_names = c('indicator', 'iso2c', 'year', 'value'),
                                           col_types = list(col_character(), col_character(), col_double(), col_double())))
  }
)

cargar POVERTY

Poverty <- read_excel("GlobalExtremePovertyDollaraDay_Compact.xlsx", sheet = "Data Long Format")

names(Poverty) <- c("ccode", "country", "year", "value")

Poverty[Poverty=="Cape Verde"] <- "Cabo Verde"
Poverty[Poverty=="Congo"] <- "Congo, Rep."
Poverty[Poverty=="Egypt"] <- "Egypt, Arab Rep."
Poverty[Poverty=="Iran"] <- "Iran, Islamic Rep."
Poverty[Poverty=="Kyrgyzstan"] <- "Kyrgyz Republic"
Poverty[Poverty=="Laos"] <- "Lao PDR"
Poverty[Poverty=="Macedonia"] <- "North Macedonia"
Poverty[Poverty=="Russia"] <- "Russian Federation"
Poverty[Poverty=="Slovakia"] <- "Slovak Republic"
Poverty[Poverty=="South Korea"] <- "Korea, Rep."
Poverty[Poverty=="Swaziland"] <- "Eswatini"
Poverty[Poverty=="Syria"] <- "Syrian Arab Republic"
Poverty[Poverty=="The Gambia"] <- "Gambia, The"
Poverty[Poverty=="Turkey"] <- "Turkiye"
Poverty[Poverty=="Venezuela"] <- "Venezuela, RB"
Poverty[Poverty=="Yemen"] <- "Yemen, Rep."

Poverty <- Poverty %>%
  filter(year > 1994) %>%
  merge(WDI_data$country, all.x = TRUE) %>%
  mutate(indicator = 'POV') %>%
  merge(my_countries) %>%
  select(indicator, iso2c, year, value)

Manipulacion de Datos

Transformar la estructura de los datos para una mejor comprension

datos_paper <- rbind(datos_WB, datos_HDI %>% select(indicator, iso2c, year, value), Poverty) %>%
  pivot_wider(names_from = indicator, values_from = value)

Revisar que datos estan como faltantes

ODA

vis_dat(datos_paper %>% select(all_of(gsub("_", ".", oda_indicators)))) 

  # DT.ODA.OATL.CD and DT.ODA.OATL.KD faltan
  # DT.ODA.ODAT.GI.ZS, DT.ODA.ODAT.GN.ZS, DT.ODA.ODAT.MP.ZS and DT.ODA.ODAT.XP.ZS tienen faltas
  # Un par de ocurrencias pais-año que faltan datos

GDP

vis_dat(datos_paper %>% select(NY.GDP.PCAP.CN, NY.GDP.PCAP.CD)) 

  # NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, NY.GDP.MKTP.CD, NY.GDP.MKTP.CN son buenos candidatos para usar como variables, 
  # 'SY'falta PIB per Capita en 2022, 2023 sin datos algunos paises

GOV

vis_dat(datos_paper %>% arrange(year) %>% select(all_of(gsub("_", ".", gob_indicators)))) 

  # Datos del 2000 para atras tienen espacios faltantes 

HDI

vis_dat(datos_paper %>% select(all_of(hdi_indicators))) 

  # abr, co2_prod, le, le_f, le_m, mmr son las pocas categorias sin datos faltantes
  # hdi faltante en multiples ocaciones

POP.GROW

vis_dat(datos_paper %>% arrange(iso2c) %>% select(SP.POP.GROW)) 

  # ZW no tiene datos de crecimiento poblacional

POV

vis_dat(datos_paper %>% arrange(iso2c) %>% select(POV)) 

  # Hay muchos paises sin datos

2001 - 2022

Tomando en cuenta los datos faltantes, hacer filtros para seleccionar una muestra mas pequeña

vis_dat(datos_paper %>% 
        filter(!iso2c %in% c('SS', 'ZW', 'BT', 'ER', 'GW', 'KP', 'LB', 'NG', 'PS', 'SO', 'VU', 'FM', 'KI', 'TL', 'CV', 'SB','SY'),
               !year %in% c(1995, 1996, 1997, 1998, 1999, 2000, 2023)) %>%
        select(iso2c, year, hdi, DT.ODA.ALLD.CD, DT.ODA.ALLD.KD, DT.ODA.ODAT.CD, DT.ODA.ODAT.KD, DT.ODA.ODAT.PC.ZS, 
               NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, SP.POP.GROW, POV, all_of(gsub("_", ".", gob_indicators)), 
               ))

2002 - 2002

vis_dat(datos_paper %>% 
        filter(!iso2c %in% c('SS', 'ZW', 'BT', 'ER', 'GW', 'KP', 'LB', 'NG', 'PS', 'SO', 'VU', 'FM', 'KI', 'TL', 'CV', 'SB','SY'),
               !year %in% c(1995, 1996, 1997, 1998, 1999, 2000, 2001, 2023)) %>%
        select(iso2c, year, hdi, DT.ODA.ALLD.CD, DT.ODA.ALLD.KD, DT.ODA.ODAT.CD, DT.ODA.ODAT.KD, DT.ODA.ODAT.PC.ZS, 
               NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, SP.POP.GROW, POV, all_of(gsub("_", ".", gob_indicators)) 
               ))

Operador Diferencia

De 2232 observaciones reducimos a 1260 (2002 hasta 2022) o a 1320 (2001 hasta 2022)

Aplicar Operador diferencia

datos_model <- datos_paper %>% 
        filter(!iso2c %in% c('SS', 'ZW', 'BT', 'ER', 'GW', 'KP', 'LB', 'NG', 'PS', 'SO', 'VU', 'FM', 'KI', 'TL', 'CV', 'SB', 'SY'),
               !year %in% c(1995, 1996, 1997, 1998, 1999, 2000, 2023)) %>%
        select(iso2c, year, hdi, DT.ODA.ALLD.CD, DT.ODA.ALLD.KD, DT.ODA.ODAT.CD, DT.ODA.ODAT.KD, DT.ODA.ODAT.PC.ZS, 
               NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, SP.POP.GROW, all_of(gsub("_", ".", gob_indicators))
               )

datos_model <- datos_model %>% arrange(iso2c, year) %>% 
        mutate(hdi_diff = hdi - dplyr::lag(hdi), 
               NY.GDP.PCAP.CD_diff = NY.GDP.PCAP.CD - dplyr::lag(NY.GDP.PCAP.CD),
               DT.ODA.ALLD.CD_diff = DT.ODA.ALLD.CD - dplyr::lag(DT.ODA.ALLD.CD),
               DT.ODA.ODAT.PC.ZS_diff = DT.ODA.ODAT.PC.ZS - dplyr::lag(DT.ODA.ODAT.PC.ZS)) %>%
        filter(!year %in% c(2001))

vis_dat(datos_model)

Promedio de Indices de Gobernanza

datos_model <- datos_model %>%
  mutate(GOV =  (CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST) / 6)

vis_dat(datos_model)

Visualizacion de datos

Relaciones

Se revisara las relaciones entre las variables graficamente No se ve una relacion clara, hay tanto paises con punteos altos y bajos de GOB que tienen tanto HID altos o bajos Quiza puede verse una leve relacion de mayor punteo en GOB acompañado de mejor punteo den HDI Los datos de GPD si muestran una relacion positiva con el HDI visto en las graficas

HDI

my_plot  <- list()

for (col in c('DT.ODA.ALLD.CD', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff', 
              'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST')) {
  my_plot[[col]] <- plot_ly(x = datos_model[[col]], y = datos_model[['hdi']], type = 'scatter', mode = 'markers', name = col)  
}
subplot(my_plot[1:4], nrows = 2, margin = 0.05) %>% layout(title = 'HDI vs ODA')
subplot(my_plot[5:10], nrows = 2, margin = 0.05) %>% layout(title = 'HDI vs GOB')

HDI diff

my_plot  <- list()

for (col in c('DT.ODA.ALLD.CD', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff', 
              'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST')) {
  my_plot[[col]] <- plot_ly(x = datos_model[[col]], y = datos_model[['hdi_diff']], type = 'scatter', mode = 'markers', name = col)  
}
subplot(my_plot[1:4], nrows = 2, margin = 0.05) %>% layout(title = 'HDI diff vs ODA')
subplot(my_plot[5:10], nrows = 2, margin = 0.05) %>% layout(title = 'HDI diff vs GOB')

GPD.PC

my_plot  <- list()
for (col in c('DT.ODA.ALLD.CD', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff', 
              'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST')) {
  my_plot[[col]] <- plot_ly(x = datos_model[[col]], y = datos_model[['NY.GDP.PCAP.CD']], 
                            type = 'scatter', mode = 'markers', name = col)  
}
subplot(my_plot[1:4], nrows = 2, margin = 0.05) %>% layout(title = 'GDP.PC vs ODA')
subplot(my_plot[5:10], nrows = 2, margin = 0.05) %>% layout(title = 'GDP.PC vs GOB')

GPD.PC diff

my_plot  <- list()
for (col in c('DT.ODA.ALLD.CD', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff', 
              'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST')) {
  my_plot[[col]] <- plot_ly(x = datos_model[[col]], y = datos_model[['NY.GDP.PCAP.CD_diff']], type = 'scatter', mode = 'markers', name = col)  
}
subplot(my_plot[1:4], nrows = 2, margin = 0.05) %>% layout(title = 'GDP.PC diff vs ODA')
subplot(my_plot[5:10], nrows = 2, margin = 0.05) %>% layout(title = 'GDP.PC diff vs GOB')

Visualizacion de Datos historia

Se realizara el mismo proceso con el crecimiento o decrecimiento de HDI anual Viendo la historia de las variables en el tiempo (por pais)

datos_model %>% filter(iso2c == 'AF') %>% plot_ly(x = ~year) %>% 
  add_trace(y = ~hdi, type = 'scatter', mode = 'lines+markers', name = 'hdi') %>% 
  add_trace(y = ~NY.GDP.PCAP.CD / 1000, type = 'scatter', mode = 'lines+markers', name = 'gdp.pc') %>% 
  add_trace(y = ~DT.ODA.ALLD.CD / 10000000000, type = 'scatter', mode = 'lines+markers', name = 'ODA.ALL')  %>% 
  add_trace(y = ~DT.ODA.ODAT.PC.ZS / 1000, type = 'scatter', mode = 'lines+markers', name = 'ODA.PC')  %>% 
  add_trace(y = ~CC.EST, type = 'scatter', mode = 'lines+markers', name = 'CC') %>% 
  add_trace(y = ~GE.EST, type = 'scatter', mode = 'lines+markers', name = 'GE')  %>% 
  add_trace(y = ~PV.EST, type = 'scatter', mode = 'lines+markers', name = 'PV')  %>% 
  add_trace(y = ~RQ.EST, type = 'scatter', mode = 'lines+markers', name = 'RQ')  %>% 
  add_trace(y = ~RL.EST, type = 'scatter', mode = 'lines+markers', name = 'RL')  %>% 
  add_trace(y = ~VA.EST, type = 'scatter', mode = 'lines+markers', name = 'VA')

Modelos Regresion Lineal

Probando modelos sencillos, regresion lineal, Minimos cuadrados, datos panel, HDI o GDP o sus differecias

vd <- c('hdi', 'hdi_diff', 'NY.GDP.PCAP.CD', 'NY.GDP.PCAP.CD_diff')
vi <- c('DT.ODA.ALLD.CD', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ODAT.PC.ZS_diff')
models <- list()
keys <- character()
for (vd_ in vd) {
  for (vi_ in vi) {
    key <- paste(vd_, vi_, sep = "~")
    keys <- c(keys, key)
    #f <- paste(vd_, '~', vi_, '+ CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW')
    #f <- paste(vd_, '~', vi_, '+ CC.PER.RNK + GE.PER.RNK + PV.PER.RNK + RQ.PER.RNK + RL.PER.RNK + VA.PER.RNK + SP.POP.GROW')
    f <- paste(vd_, '~', vi_, '+ GOV + SP.POP.GROW')
    models[[key]] <- lm(f, data=datos_model)
  }
}

HDI

ODA.ALL

HDI = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[1]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23055 -0.05445 -0.00579  0.05112  0.56380 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     6.703e-01  6.959e-03   96.33  < 2e-16 ***
## DT.ODA.ALLD.CD  1.115e-11  2.353e-12    4.74 2.38e-06 ***
## GOV             7.339e-02  5.517e-03   13.30  < 2e-16 ***
## SP.POP.GROW    -4.422e-02  2.551e-03  -17.34  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08798 on 1256 degrees of freedom
## Multiple R-squared:  0.3091, Adjusted R-squared:  0.3074 
## F-statistic: 187.3 on 3 and 1256 DF,  p-value: < 2.2e-16
# Todas las variables son significativas al 99% excepto Regulatory Quality

ODA.ALL_diff

HDI = ODA.ALL_diff + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[2]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23315 -0.05477 -0.00606  0.05287  0.57332 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          6.762e-01  6.907e-03   97.91   <2e-16 ***
## DT.ODA.ALLD.CD_diff  3.402e-13  4.848e-12    0.07    0.944    
## GOV                  6.966e-02  5.511e-03   12.64   <2e-16 ***
## SP.POP.GROW         -4.305e-02  2.563e-03  -16.80   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08876 on 1256 degrees of freedom
## Multiple R-squared:  0.2967, Adjusted R-squared:  0.295 
## F-statistic: 176.6 on 3 and 1256 DF,  p-value: < 2.2e-16
# Todas las variables son significativas al 99% excepto ODA.ALL_diff y Regulatory Quality

ODA.PC

HDI = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[3]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.22974 -0.05449 -0.00562  0.05266  0.56145 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        6.697e-01  7.684e-03  87.153   <2e-16 ***
## DT.ODA.ODAT.PC.ZS  6.806e-05  3.549e-05   1.918   0.0554 .  
## GOV                6.627e-02  5.776e-03  11.475   <2e-16 ***
## SP.POP.GROW       -4.327e-02  2.560e-03 -16.900   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08863 on 1256 degrees of freedom
## Multiple R-squared:  0.2988, Adjusted R-squared:  0.2971 
## F-statistic: 178.4 on 3 and 1256 DF,  p-value: < 2.2e-16
# Todas las variables son significativas al 99% excepto Regulatory Quality

ODA.PC_diff

HDI = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[4]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23342 -0.05470 -0.00614  0.05312  0.57610 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             6.762e-01  6.906e-03  97.919   <2e-16 ***
## DT.ODA.ODAT.PC.ZS_diff -1.942e-05  6.719e-05  -0.289    0.773    
## GOV                     6.965e-02  5.508e-03  12.644   <2e-16 ***
## SP.POP.GROW            -4.303e-02  2.561e-03 -16.801   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08876 on 1256 degrees of freedom
## Multiple R-squared:  0.2968, Adjusted R-squared:  0.2951 
## F-statistic: 176.7 on 3 and 1256 DF,  p-value: < 2.2e-16
# Todas las variables son significativas al 99% excepto  ODA.ALL_diff y Regulatory Quality

HDI_diff

ODA.ALL

HDI_diff = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[5]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.081219 -0.002722  0.000275  0.003123  0.075333 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     4.860e-03  5.067e-04   9.591   <2e-16 ***
## DT.ODA.ALLD.CD -3.312e-13  1.713e-13  -1.933   0.0534 .  
## GOV             7.315e-05  4.017e-04   0.182   0.8555    
## SP.POP.GROW     3.494e-04  1.857e-04   1.881   0.0602 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.006406 on 1256 degrees of freedom
## Multiple R-squared:  0.005273,   Adjusted R-squared:  0.002897 
## F-statistic: 2.219 on 3 and 1256 DF,  p-value: 0.08419
# Todas las variables son significativas al 95% excepto ODA.ALL, Control of Corruption y Rule of Law

ODA.ALL_diff

HDI_diff = ODA.ALL_diff + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[6]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.080152 -0.002721  0.000216  0.003098  0.073931 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          4.667e-03  4.980e-04   9.373   <2e-16 ***
## DT.ODA.ALLD.CD_diff -8.802e-13  3.496e-13  -2.518   0.0119 *  
## GOV                  1.579e-04  3.973e-04   0.397   0.6912    
## SP.POP.GROW          3.306e-04  1.848e-04   1.789   0.0739 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0064 on 1256 degrees of freedom
## Multiple R-squared:  0.007324,   Adjusted R-squared:  0.004953 
## F-statistic: 3.089 on 3 and 1256 DF,  p-value: 0.0263
# Todas las variables son significativas al 95% excepto Control of Corruption y Voice and Accountability

ODA.PC

HDI_diff = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[7]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.078807 -0.002680  0.000203  0.002958  0.076519 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        5.834e-03  5.514e-04  10.581  < 2e-16 ***
## DT.ODA.ODAT.PC.ZS -1.204e-05  2.546e-06  -4.729 2.51e-06 ***
## GOV                7.817e-04  4.144e-04   1.886   0.0595 .  
## SP.POP.GROW        3.549e-04  1.837e-04   1.932   0.0536 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.006359 on 1256 degrees of freedom
## Multiple R-squared:  0.01977,    Adjusted R-squared:  0.01743 
## F-statistic: 8.443 on 3 and 1256 DF,  p-value: 1.481e-05
# Todas las variables son significativas al 95% excepto Rule of Law y Voice and Accountability

ODA.PC_diff

HDI_diff = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[8]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.078419 -0.002772  0.000199  0.003081  0.072676 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             4.697e-03  4.966e-04   9.458  < 2e-16 ***
## DT.ODA.ODAT.PC.ZS_diff -1.747e-05  4.832e-06  -3.616 0.000312 ***
## GOV                     1.843e-04  3.961e-04   0.465 0.641872    
## SP.POP.GROW             3.212e-04  1.842e-04   1.744 0.081445 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.006383 on 1256 degrees of freedom
## Multiple R-squared:  0.01259,    Adjusted R-squared:  0.01023 
## F-statistic: 5.338 on 3 and 1256 DF,  p-value: 0.001178
# Todas las variables son significativas al 95% excepto Control of Corruption y Voice and Accountability

GPD.PC

ODA.ALL

GPD.PC = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[9]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2076.9  -674.3  -222.5   453.4  4866.6 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     2.506e+03  7.360e+01  34.046   <2e-16 ***
## DT.ODA.ALLD.CD  3.534e-08  2.488e-08   1.420    0.156    
## GOV             7.775e+02  5.835e+01  13.326   <2e-16 ***
## SP.POP.GROW    -2.673e+02  2.698e+01  -9.908   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 930.5 on 1256 degrees of freedom
## Multiple R-squared:  0.207,  Adjusted R-squared:  0.2051 
## F-statistic: 109.3 on 3 and 1256 DF,  p-value: < 2.2e-16
# Todas las variables son significativas al 99% excepto ODA.ALL, Control of Corruption, Regulatory Quality y Rule of Law

ODA.ALL_diff

GPD.PC = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[10]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2093.1  -670.6  -221.3   444.9  4914.4 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          2.524e+03  7.246e+01  34.836   <2e-16 ***
## DT.ODA.ALLD.CD_diff -1.505e-08  5.086e-08  -0.296    0.767    
## GOV                  7.652e+02  5.781e+01  13.237   <2e-16 ***
## SP.POP.GROW         -2.633e+02  2.689e+01  -9.792   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 931.2 on 1256 degrees of freedom
## Multiple R-squared:  0.2058, Adjusted R-squared:  0.2039 
## F-statistic: 108.5 on 3 and 1256 DF,  p-value: < 2.2e-16
# Todas las variables son significativas al 99% excepto ODA.ALL_diff, Control of Corruption, Regulatory Quality y Rule of Law

ODA.PC

GPD.PC = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[11]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1976.9  -650.5  -220.3   430.6  4496.0 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       2310.9576    79.5755   29.04  < 2e-16 ***
## DT.ODA.ODAT.PC.ZS    2.2345     0.3675    6.08 1.59e-09 ***
## GOV                654.8088    59.8090   10.95  < 2e-16 ***
## SP.POP.GROW       -271.0975    26.5131  -10.22  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 917.8 on 1256 degrees of freedom
## Multiple R-squared:  0.2285, Adjusted R-squared:  0.2266 
## F-statistic:   124 on 3 and 1256 DF,  p-value: < 2.2e-16
# Todas las variables son significativas al 99% excepto Rule of Law

ODA.PC_diff

GPD.PC = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[12]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2092.4  -667.9  -221.6   449.1  4914.1 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            2524.5201    72.4553  34.842   <2e-16 ***
## DT.ODA.ODAT.PC.ZS_diff   -0.1304     0.7049  -0.185    0.853    
## GOV                     765.6854    57.7923  13.249   <2e-16 ***
## SP.POP.GROW            -263.4905    26.8720  -9.805   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 931.2 on 1256 degrees of freedom
## Multiple R-squared:  0.2058, Adjusted R-squared:  0.2039 
## F-statistic: 108.5 on 3 and 1256 DF,  p-value: < 2.2e-16
# Todas las variables son significativas al 99% excepto ODA.ALL_diff, Control of Corruption, Regulatory Quality y Rule of Law

GPD.PC_diff

ODA.ALL

GPD.PC_diff = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[13]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1823.28   -54.66    -8.44    57.85   998.20 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.171e+02  1.429e+01   8.197 6.01e-16 ***
## DT.ODA.ALLD.CD  3.018e-09  4.830e-09   0.625  0.53222    
## GOV             2.807e+01  1.133e+01   2.479  0.01331 *  
## SP.POP.GROW    -1.710e+01  5.237e+00  -3.265  0.00113 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 180.6 on 1256 degrees of freedom
## Multiple R-squared:  0.01551,    Adjusted R-squared:  0.01316 
## F-statistic: 6.596 on 3 and 1256 DF,  p-value: 0.0002014
# Todas las variables son significativas al 95% excepto ODA.ALL, Control of Corruption, Regulatory Quality y Voice and Accountability

ODA.ALL_diff

GPD.PC_diff = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[14]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1825.99   -54.34    -9.01    57.16   995.05 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          1.187e+02  1.406e+01   8.447  < 2e-16 ***
## DT.ODA.ALLD.CD_diff  1.283e-09  9.867e-09   0.130  0.89653    
## GOV                  2.710e+01  1.121e+01   2.417  0.01581 *  
## SP.POP.GROW         -1.680e+01  5.216e+00  -3.221  0.00131 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 180.6 on 1256 degrees of freedom
## Multiple R-squared:  0.01522,    Adjusted R-squared:  0.01287 
## F-statistic: 6.469 on 3 and 1256 DF,  p-value: 0.0002407
# Todas las variables son significativas al 95% excepto ODA.ALL_diff, Control of Corruption, Regulatory Quality y Voice and Accountability

ODA.PC

GPD.PC_diff = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[15]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1828.11   -54.96    -9.29    58.56   992.60 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       123.36451   15.65873   7.878 7.14e-15 ***
## DT.ODA.ODAT.PC.ZS  -0.04877    0.07232  -0.674  0.50021    
## GOV                29.48222   11.76912   2.505  0.01237 *  
## SP.POP.GROW       -16.61104    5.21721  -3.184  0.00149 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 180.6 on 1256 degrees of freedom
## Multiple R-squared:  0.01556,    Adjusted R-squared:  0.01321 
## F-statistic: 6.618 on 3 and 1256 DF,  p-value: 0.0001953
# Todas las variables son significativas al 95% excepto ODA.PC, Control of Corruption, Political Stability, Regulatory Quality y Voice and Accountability

ODA.PC_diff

GPD.PC_diff = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW

summary(models[[keys[16]]])
## 
## Call:
## lm(formula = f, data = datos_model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1826.13   -54.92    -9.07    56.70   995.26 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            118.64090   14.05293   8.442  < 2e-16 ***
## DT.ODA.ODAT.PC.ZS_diff   0.08499    0.13672   0.622  0.53430    
## GOV                     27.06141   11.20899   2.414  0.01591 *  
## SP.POP.GROW            -16.80985    5.21191  -3.225  0.00129 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 180.6 on 1256 degrees of freedom
## Multiple R-squared:  0.01551,    Adjusted R-squared:  0.01316 
## F-statistic: 6.595 on 3 and 1256 DF,  p-value: 0.0002018
# Todas las variables son significativas al 95% excepto ODA.PC_diff, Control of Corruption, Regulatory Quality y Voice and Accountability

Modelos Efectos fijos

HDI ~ ODA.ALL

plm(hdi ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW, data=datos_model,
    index = c("iso2c", "year"), model = "within")
## 
## Model Formula: hdi ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + 
##     VA.EST + SP.POP.GROW
## 
## Coefficients:
## DT.ODA.ALLD.CD         CC.EST         GE.EST         PV.EST         RQ.EST 
##     2.0890e-11    -8.3315e-03     7.5391e-03    -4.4617e-03     2.0938e-02 
##         RL.EST         VA.EST    SP.POP.GROW 
##     3.2560e-02     6.9264e-03    -5.5903e-03
# summary(lm(hdi ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Corruption Control, Government Effectiveness, Political Stability y Voice and Accountability no son significativas

HDI ~ ODA.PC

plm(hdi ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW, data=datos_model,
    index = c("iso2c", "year"), model = "within")
## 
## Model Formula: hdi ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + 
##     RL.EST + VA.EST + SP.POP.GROW
## 
## Coefficients:
## DT.ODA.ODAT.PC.ZS            CC.EST            GE.EST            PV.EST 
##         0.0001890        -0.0090492        -0.0012169        -0.0066846 
##            RQ.EST            RL.EST            VA.EST       SP.POP.GROW 
##         0.0225845         0.0433849         0.0048044        -0.0089386
#summary(lm(hdi ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Corruption Control, Government Effectiveness y Voice and Accountability no son significativas

GDP.PC ~ ODA.ALL

plm(NY.GDP.PCAP.CD ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW, data=datos_model,
    index = c("iso2c", "year"), model = "within")
## 
## Model Formula: NY.GDP.PCAP.CD ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + 
##     RQ.EST + RL.EST + VA.EST + SP.POP.GROW
## 
## Coefficients:
## DT.ODA.ALLD.CD         CC.EST         GE.EST         PV.EST         RQ.EST 
##     2.6336e-07    -2.1804e+01     4.6280e+02     1.9040e+02    -5.2602e+01 
##         RL.EST         VA.EST    SP.POP.GROW 
##    -1.6883e+02    -2.6173e+01    -3.7759e+01
#summary(lm(NY.GDP.PCAP.CD ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Intercepto, Corruption Control, Regulatory Quality, Rule of Law, Voice and Accountability y Population Growth no son significativas

GDP.PC ~ ODA.PC

plm(NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW, data=datos_model,
    index = c("iso2c", "year"), model = "within")
## 
## Model Formula: NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + 
##     RQ.EST + RL.EST + VA.EST + SP.POP.GROW
## 
## Coefficients:
## DT.ODA.ODAT.PC.ZS            CC.EST            GE.EST            PV.EST 
##            3.6490          -82.8703          370.2174          156.9927 
##            RQ.EST            RL.EST            VA.EST       SP.POP.GROW 
##          -13.5138           -2.0353          -66.6104          -85.1696
#summary(lm(NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Corruption Control, Regulatory Quality, Rule of Law y Voice and Accountability no son significativas